activity
20192026
most citedLearning to Orient Surfaces by Self-supervised Spherical CNNs

9 citations · 31 across the 33 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

cs.CV2025

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing

Adrian Poniatowski, Natalie Gentner, Manuel Barusco +3

In the semiconductor sector, due to high demand but also strong and increasing competition, time to market and quality are key factors in securing significant market share in vario…

cs.LG2025

Additive decomposition of one-dimensional signals using Transformers

Samuele Salti, Andrea Pinto, Alessandro Lanza +1

One-dimensional signal decomposition is a well-established and widely used technique across various scientific fields. It serves as a highly valuable pre-processing step for data a…

cs.CV2025

NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfaces

Pierluigi Zama Ramirez, Fabio Tosi, Luigi Di Stefano +36

This paper reports on the NTIRE 2025 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhanc…

cs.CV2025

Scaling LLaNA: Advancing NeRF-Language Understanding Through Large-Scale Training

Andrea Amaduzzi, Pierluigi Zama Ramirez, Giuseppe Lisanti +2

Recent advances in Multimodal Large Language Models (MLLMs) have shown remarkable capabilities in understanding both images and 3D data, yet these modalities face inherent limitati…

cs.CV2025★ 3 cited

RendBEV: Semantic Novel View Synthesis for Self-Supervised Bird's Eye View Segmentation

Henrique Piñeiro Monteagudo, Leonardo Taccari, Aurel Pjetri +2

Bird's Eye View (BEV) semantic maps have recently garnered a lot of attention as a useful representation of the environment to tackle assisted and autonomous driving tasks. However…

cs.CV2025

Weight Space Representation Learning on Diverse NeRF Architectures

Francesco Ballerini, Pierluigi Zama Ramirez, Luigi Di Stefano +1

Neural Radiance Fields (NeRFs) have emerged as a groundbreaking paradigm for representing 3D objects and scenes by encoding shape and appearance information into the weights of a n…